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AI DNA Similarity

Best Alternatives to Ederson

Players most similar to Ederson (Goalkeeper, €13.0M) — ranked by AI DNA similarity score across playing style, pressing intensity, and tactical fit.

Top 3 Alternatives to Ederson

  1. 1.André Onana87% DNA match·Trabzonspor€15.0M
  2. 2.Ivo Grbic86% DNA match·Fatih Karagümrük€3.5M
  3. 3.Alisson85% DNA match·Liverpool€17.0M

Ranked by AI DNA similarity — 768 dimensions across playing style, pressing intensity, and tactical fit.

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Intelligence Verdict

Aerials WonTop 9%
???Bottom 0%

Ederson has carved out a reputation in the Super Lig as one of its most technically assured goalkeepers...

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Playing Style Analysis

Traditional Keeper

Ederson has carved out a reputation in the Super Lig as one of its most technically assured goalkeepers, a shot-stopper who doubles as a genuine first-pass option in a league where that combination is rarer than it sounds. His 79.9% pass accuracy places him in the top 5% among league peers—not a trivial achievement when defensive pressure in the Super Lig can be relentless and unforgiving. The counterintuitive detail here is his passes-per-90 sitting below average: he's not spraying the ball constantly, but when he does pass, he's almost always right. The three most similar players to Ederson by playing style are:

  • André Onana(87% match)Onana is the rare goalkeeper who functions less like a last line of defence and more like an auxiliary midfielder wearing gloves. His 80.3% pass accuracy places him in the top 5% of Super Lig goalkeepers, but the more revealing number is his 3.81 passes into the final third per 90—he isn't just recycling possession, he's actively advancing it. The counterintuitive detail: his 0.10 key passes and 0.05 assists per 90 both rank top 5% in the league, meaning this goalkeeper is, statistically, a chance creator.
  • Ivo Grbic(86% match)Grbic is a statistical anomaly in the Super Lig, operating less like a traditional shot-stopper and more like a deep-lying libero who happens to wear gloves. While his 1,980 minutes prove his durability, it is his 73.8% pass accuracy—placing him in the elite top 5% of the league—that defines his profile as a modern distributor. He doesn't just clear the ball; he initiates, evidenced by his 2.58 passes into the final third per 90.
  • Alisson(85% match)A Traditional Keeper. Statistically, he stands out as dominant in aerial duels (92% success) and commands the box with authority (0.8 punches/90).

Transfer Intelligence

André Onana delivers 87% of the same playing style, at a 15% premium over Ederson, and is 30 years old.

Similarity is calculated using per-90 performance data across multiple playing style dimensions. How Player DNA matching works →

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Comparison Base
Ederson
GoalkeeperBrazil€13.0M
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Similar Players — Ranked by DNA Similarity

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Our 768-dimension Player DNA model matches playing style, physical profile, pressing intensity, and tactical fit. Ask the AI to explain exactly what makes these players statistically similar to Ederson.

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Frequently Asked Questions

Who are the best alternatives to Ederson?
The top alternatives to Ederson based on AI DNA playing style analysis include: André Onana, Ivo Grbic, Alisson, Odysseas Vlachodimos, Diogo Costa. These players were matched using Rising Transfers' 768-dimension DNA model across playing style, pressing intensity, and tactical fit — not just position or market value.
Which players are similar to Ederson in 2026?
Players with a similar profile to Ederson in 2026 include André Onana (€15.0M), Ivo Grbic (€3.5M), Alisson (€17.0M). For a deeper DNA-level comparison including playing style, physical attributes, and tactical fit, ask Rising Transfers' AI directly.
What position does Ederson play and who plays similarly?
Ederson plays as a Goalkeeper. Players with a comparable positional profile include André Onana (Cameroon, €15.0M); Ivo Grbic (Croatia, €3.5M); Alisson (Brazil, €17.0M); Odysseas Vlachodimos (Greece, €4.0M).
How does Rising Transfers find similar players?
Rising Transfers uses a proprietary 768-dimension Player DNA model trained on 3.2 million match events. Each player is represented as a vector across 35+ per-90 metrics including pressing intensity, passing footprint, dribbling profile, and defensive contribution. Similarity is measured using cosine distance — the same technique used in state-of-the-art AI systems — making it the most precise player comparison tool available publicly.